# Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas–Solid Fluidized Beds

## TL;DR

This paper presents a machine learning method to accurately identify and track bubbles in gas-solid fluidized beds, improving the study of bubble dynamics.

## Contribution

A novel ML-assisted image segmentation method with high accuracy for bubble identification in fluidized beds.

## Key findings

- The ML method achieves 98.75% accuracy with minimal training data.
- It effectively handles uncertainties like varying illumination and out-of-focus regions.
- The method reveals new insights into bubble dynamics in oscillating beds.

## Abstract

This study introduces a machine learning (ML)-assisted
image segmentation
method for automatic bubble identification in gas–solid quasi-2D
fluidized beds, offering enhanced accuracy in bubble recognition.
Binary images are segmented by the ML method, and an in-house Lagrangian
tracking technique is developed to track bubble evolution. The ML-assisted
segmentation method requires few training data, achieves an accuracy
of 98.75%, and allows for filtering out common sources of uncertainty
in hydrodynamics, such as varying illumination conditions and out-of-focus
regions, thus providing an efficient tool to study bubbling in a standard,
consistent, and repeatable manner. In this work, the ML-assisted methodology
is tested in a particularly challenging case: structured oscillating
fluidized beds, where the spatial and time evolution of the bubble
position, velocity, and shape are characteristics of the nucleation-propagation-rupture
cycle. The new method is validated across various operational conditions
and particle sizes, demonstrating versatility and effectiveness. It
shows the ability to capture challenging bubbling dynamics and subtle
changes in velocity and size distributions observed in beds of varying
particle size. New characteristic features of oscillating beds are
identified, including the effect of frequency and particle size on
the bubble morphology, aspect, and shape factors and their relationship
with the stability of the flow, quantified through the rate of coalescence
and splitting events. This type of combination of classic analysis
with the application of the ML assisted techniques provides a powerful
tool to improve standardization and address the reproducibility of
hydrodynamic studies, with the potential to be extended from gas–solid
fluidization to other multiphase flow systems.